Situation Awareness Model Research Articles

Human-computer interaction interface design of flight simulator based on situation awareness

To meet the comprehensive requirements of drivers for Human-Computer Interaction (HCI) during operation and to enhance situation awareness (SA), this paper proposes a design and evaluation method for flight simulator HCI interfaces grounded in SA. Firstly, the widely adopted Endsley’s SA model, which comprehensively covers processes relevant to individual drivers was selected as the foundational framework. The study concentrates on flight, cruise, and landing scenarios within the simulator, analyzing and restructuring the information architecture of the HCI interface. Secondly, a self-assessment scale was developed based on the three-level model of information processing for SA to measure drivers’ SA cognitive levels. The core scenarios of the simulation training were analyzed to establish a design-making process. Thirdly, invite participants were invited to engage in simulated driving sessions on the flight simulator, where their SA cognitive levels were assessed. An analysis of detail features was performed on samples with high SA cognitive levels. Subsequently, drivers’ requirements for the control layout features of the HCI interface design were gathered through surveys. The design detail features of the HCI interface associated with high SA cognitive levels were identified and synthesized into four distinct dimensions to guide the detailed design process. Finally, participants engaged in simulated driving using the finalized interface design, and their SA cognitive levels were reassessed. The results demonstrated that the HCI interface design derived from this method significantly improved SA cognitive levels compared to the original design.

Factors Affecting Control Room Operator Situation Awareness and ITS Fuzzy Logic Model

Factors Affecting Control Room Operator Situation Awareness and ITS Fuzzy Logic Model

Improving Situation Awareness in Partially Automated Vehicles: The Effectiveness of Redesigned Visual Signals

Visual signals that integrate situation awareness (SA) information can improve drivers’ performance when assuming control of a partially automated vehicle from a non-driving task. By incorporating information that follows the levels of model of SA, these signals enable drivers to more quickly attain higher levels of SA, potentially bypassing traditional sequential processing stages. We evaluated the effectiveness of such SA-enhanced signals (displayed in the instrument cluster) in a simulated driving study by measuring takeover times and collision occurrences. The results indicate that incorporating SA information about the environment significantly reduces the time needed for drivers to take control of the vehicle and results in fewer collisions. Considering these performance differences suggests insights on optimizing visual cues to quickly restore SA among drivers as they transition from non-driving activities to manual control in partially automated vehicles.

Agent-Based Model for Situational Awareness in the Workplace: Enhancing Neural Networks with Direct Feedback Alignment

The integration of direct feedback alignment (DFA) into neural networks presents a paradigm shift in computational models, enhancing situational awareness within professional settings. This paper explores the application of DFA in agent-based computational models, demonstrating its efficiency and biological plausibility over traditional backpropagation methods. The research highlights the significant impact of direct feedback alignment on reinforcing situational awareness, evidenced through comprehensive simulations that show marked improvements in agents' ability to effectively navigate and comprehend complex work environments. The study suggests that direct feedback alignment revolutionizes neural network learning processes and significantly enhances cognitive aspects critical to employee safety and operational efficiency. It lays the groundwork for future investigations into the integration of neural computation techniques with organizational psychology and behavior, offering a new perspective on fostering safer, more aware, and efficient workplace environments. The potential application of direct feedback alignment across various professional scenarios opens new avenues for research in computational neuroscience, cognitive psychology, and organizational behavior, with a focus on optimizing human-environment interactions in complex systems.

Research on standardization of power transformer monitoring and early warning based on multi-source data

To meet the growing demand for integrated monitoring of complex power grid equipment, it is necessary to improve the situational awareness model of power transformers. The model is expected to assist monitoring personnel in timely identifying transformers with deteriorating trends among massive and discrete monitoring information, and to make responses in advance. However, the current transformer state awareness technology generally has the problem of single data source and poor timeliness, and still requires monitoring personnel to make artificial analysis and prediction in combination with telemetry information, which cannot fully meet the requirements of power grid equipment monitoring. This paper is based on multi-source data fusion technology, through associating and mining transformer alarm information, equipment maintenance records and power transmission and transformation online monitoring data, to extract the dimension features of transformer operation situation assessment. By constructing a multi-layer perceptron model, a transformer state transition model based on the principle of Markov chain is established, which can predict possible defects 2 h in advance and achieve good results, and determine the transformer state early warning index, providing sufficient time for monitoring personnel to deploy transformer operation and maintenance work in advance. Finally, the effectiveness of the method proposed in this paper is proved by the case of transformer crisis state in a city substation, and the method proposed in this paper has important significance for transformer state early warning.

The awareness of operators: a goal-directed task analysis in SOCs for critical infrastructure

Security operation centers (SOCs) are increasingly established to meet the growing threat against cyber security. The operators of SOCs respond to complex incidents under time constraints. Within critical infrastructure, the consequences of human error or low performance in SOCs may be detrimental. In other domains, situation awareness (SA) has proven useful to understand and measure how operators use information and decide the correct actions. Until now, SA research in SOCs has been restricted by a lack of in-depth studies of SA mechanisms. Therefore, this study is the first to conduct a goal-directed task analysis in a SOC for critical infrastructure. The study was conducted through a targeted series of unstructured and semi-structured interviews with SOC operators and their leaders complemented by a review of documents, incident reports, and in situ observation of work within the SOC and real incidents. Among the presented findings is a goal hierarchy alongside a complete overview of the decisions the operators make during escalated incidents. How the operators gain and use SA in these decisions is presented as a complete set of SA requirements. The findings are accompanied by an analysis of contextual differences in how the operators prioritize goals and use information in network incidents and security incidents. This enables a discussion of what SA processes might be automated and which would benefit from different SA models. The study provides a unique insight into the SA of SOC operators and is thus a steppingstone for bridging the knowledge gap of Cyber SA.

Investigation on situation awareness model of unmanned aerial vehicle groups communication network based on adversarial network

With the widespread application and development of unmanned aerial vehicle (UAV) technology, ensuring the security and stability of UAV swarm communication networks has become crucial. Given the diverse forms of interference and attacks in current networks, this poses a serious threat to the normal operation of UAV swarm communication. Therefore, how to accurately identify and effectively counter these network threats has become the focus of research. This study comprehensively evaluates the core technology of UAV swarm communication network situational awareness and constructs a situational awareness model based on adversarial networks. The model utilizes adversarial network technology and combines data collection and processing to design four experiments to comprehensively evaluate the performance of the model in different scenarios. The experimental results show that as the amount of data gradually increases, the performance of the model also improves. When processing 100, 1000, and 10,000 data points, the model achieved accuracies of 0.955, 0.962, and 0.982, respectively. Furthermore, the experimental results also indicate that effective noise suppression measures can significantly improve the accuracy and stability of the situational awareness model. Additionally, it is noted that different model structures will affect training duration, accuracy, and stability. Although increasing network scale may lead to increased computational complexity and latency, its accuracy is correspondingly improved. The adversarial network-based situational awareness model proposed in this study can accurately identify and effectively counter interference and attacks in UAV swarm communication networks, thereby providing solid protection for the collaborative combat and information sharing of UAV swarms.

Development of Situation Awareness Model in Robotic Spot-welding (RSW) System based on Sensor Data Visualization

The advancement of prognostics and health management (PHM) using the industrial internet of things (IIoT) and artificial intelligence (AI) has enabled stable mass production with assured quality. However, the manufacturing industry often faces variable conditions like design and material changes. Before applying PHM systems, it is crucial to design a system that recognizes these changes. This study developed a robust situation awareness model for a robotic spot-welding (RSW) system, resilient to reduced training data, using sensor data visualization and convolutional neural networks (CNN). Four variable situations were established based on thickness and material changes: GI steel 0.6 t, GI steel 1.0 t, mild steel 0.8 t, and GA steel 0.8 t, with GI steel 0.8 t as the reference. Data were collected using process parameters (welding current and electrode force) for each situation. A fuzzy-based energy pattern image (FEPI) visualization technique was applied to visualize energy differences in the spot-welding process from four sensors (current, voltage, electrode force, displacement). Using these techniques, thickness and material variation awareness models were constructed. The classification accuracy of CNN models trained on time-series data was evaluated to verify the effectiveness under reduced training data conditions.

Cybersecurity Situational Awareness Model using Improved LSTM-Informer

address the problem of low prediction accuracy in current network security situational prediction

Situation Awareness of Mars helicopter based on YOLOv7

Situation awareness on Mars is indispensable for various downstream applications such as navigation and path planning, mapping and scientific discover. However, current Mars rover platform suffered from the absence of global information, leading to mission reduction and the failure of global planning. Compared to the rover, Mars helicopter is a new type of attempt for Mars exploration, which can provide a wide range view of scenes to well support the above missions. To this end, in this paper we proposed a novel learning-based situation awareness model for Mars helicopter. We modified the Yolov7 model by embedding a new channel attention on its head to improve the accuracy of detection. Besides, we designed a Tiny-Scale Detection Module (TSDM) to capture small or hidden rocks in complex Martian surface. To achieve further refinement and validate our proposal, we built an open-source synthetic dataset in air view. After extensive experiments, our model achieved excellent outperforms than the baseline model in our Mars dataset.

Dynamic Model of Situation Awareness During Automated Driving

Dynamic Model of Situation Awareness During Automated Driving

A novel industrial process situation awareness model based on multi‐time scale dynamic feature fusion with applications to float glass manufacturing

AbstractModern industrial manufacturing processes often focus on the changes of performance indicators and the evolution of operation conditions. However, the dynamics of variables are vague within processes and even more difficult to be characterised across different processes. The time‐series dynamic characteristics of the different scales among variables, processes, and production cycles develop and transfer with changes in parameters, equipment, and processes. It is difficult to accurately show the quality index and operating condition trend at the same time. To solve these problems, a situation awareness (SA) framework integrating multi‐time scale dynamic features for manufacturing processes is proposed. First, data are condensed and reconstructed through the denoising long short term memory autoencoder to reveal the time series dynamic characteristics to get neighbourhood features which contain features among neighbourhood samples. Second, the neighbourhood features divided into window blocks are fused into the stage features by statistical analysis. Finally, a multi‐scale isometric convolution network is designed to extract the local and global features, which can effectively show the development of dynamic features on a long time scale, and profoundly describe the influence of full cycle features on variables and operating conditions. The proposed model is verified on the real data set of a float glass manufacturing process, and the SA model can well predict the future trend of long time series.

Research on a Critical Link Discovery Method for Network Security Situational Awareness.

Network security situational awareness (NSSA) aims to capture, understand, and display security elements in large-scale network environments in order to predict security trends in the relevant network environment. With the internet's increasingly large scale, increasingly complex structure, and gradual diversification of components, the traditional single-layer network topology model can no longer meet the needs of network security analysis. Therefore, we conduct research based on a multi-layer network model for network security situational awareness, which is characterized by the three-layer network structure of a physical device network, a business application network, and a user role network. Its network characteristics require new assessment methods, so we propose a multi-layer network link importance assessment metric: the multi-layer-dependent link entropy (MDLE). On the one hand, the MDLE comprehensively evaluates the connectivity importance of links by fitting the link-local betweenness centrality and mapping entropy. On the other hand, it relies on the link-dependent mechanism to better aggregate the link importance contributions in each network layer. The experimental results show that the MDLE has better ordering monotonicity during critical link discovery and a higher destruction efficacy in destruction simulations compared to classical link importance metrics, thus better adapting to the critical link discovery requirements of a multi-layer network topology.

Improving Situation Awareness to Advance Patient Outcomes: A Systematic Literature Review.

Improving nurses' situation awareness skills would likely improve patient status recognition and prevent adverse events. Technologies such as electronic health record dashboards can be a promising approach to support nurses' situation awareness. However, the effect of these dashboards on this skill is unknown. This systematic literature review explores the evidence around interventions to improve nurses' situation awareness at the point of care. Current research on this subject is limited. Studies that examined the use of electronic health record dashboards as an intervention had weak evidence to support their effectiveness. Other interventions, including communication interventions and structured nursing assessments, may also improve situation awareness, but more research is needed to confirm this. It is important to carefully consider the design and content of situation awareness interventions, as well as the specific outcomes being measured, when designing situation awareness interventions. Overall, there is a need for higher-quality research in this area to determine the most effective interventions for improving nurse situation awareness. Future studies should focus on developing dashboards that follow a theoretical situation awareness model information and represent all situation awareness levels.

Intelligent Situation Awareness Based on the Fractal Dimension and Multidimensional Reconstruction

AbstractIn order to realize autonomy and intelligence for situation awareness, this paper proposes an intelligent situation awareness model based on fractional dimension information mining and multidimensional information reconstruction. First, some new concepts are proposed, the spatial situation perception is established by 3D reconstruction of the input fusion information, 4D reconstruction completes the situation comprehension and 5D reconstruction seeks the situation prediction. The three‐level situation estimation model is optimized to a more robust flexible model. Combined with the database system, the reasoning learning mechanism, and the diversified human‐machine interface concept, a basic framework of intelligent situation awareness is constructed. Second, the basic process of intelligent situation awareness is provided. Third, based on the flexible configuration method of the situation awareness system and the concept of unmanned weight determination for the situation awareness agents, an efficiency measurement model for the intelligent situation awareness model is also constructed, and an evaluation method for the consistency of multinode intelligent situation awareness is also provided. Fourth, the paper gives a typical electromagnetic situation estimation example for a drilling platform to explain and validate the concepts. Finally, several suggestions are put forward for the next construction of an intelligent situation awareness system.

Research on Network Security Situation Awareness Technology Based on Security Intelligent Monitoring Technology

This paper uses data mining technology to dynamically monitor tobacco Industrial Enterprise' information systems. This paper builds an Internet security situation awareness system under a big data environment. The weight clustering method is used to classify users' network behavior. The spacing of weights is optimized to ensure the maximum difference in classification. Then, NAWL-ILSTM technology establishes a security situational awareness model for the Internet environment. In this project, the extended and short-memory Nadam optimal algorithm (NAWL) is used to realize data deep learning. Finally, the tobacco industry network security situation assessment method is designed to complete the dynamic monitoring of tobacco industry network security based on data mining. Simulation results show that the proposed method can effectively improve the safety evaluation performance of the system and reduce evaluation errors.

Industrial Process Management Model to Improve Productivity and Reduce Waste

One of the challenges facing operations management is the design, choice, and implementation of action plans adapted to the magnitude of the deviations from the variables of an industrial process. Making conscious and quick decisions is crucial to achieving improvements in productivity. This will be achieved if the organization’s internal and external communication model is strategically designed, considering specific cultural factors and the symmetry or asymmetry required in the communication model. However, how do we organize ourselves and through what channels do we communicate within a production process to generate Big Data that combines data from technologies and the perception, comprehension, and projection of experienced humans? Our hypothesis suggests that the implementation of our model generates a continuous improvement system that could provide significant benefits to the company by connecting management with the place where the work happens (Gemba), streamlining multiple processes, thereby fostering sustainability. The developed model, “Group Situational Awareness Model”, was implemented in an operations management team, following the guidelines of an action researcher methodology. This resulted in the design of an operations management model and a detailed methodology for its implementation, achieving significant improvements in the metrics of the current process, making it a success story.

Heritage buildings management: the role of situational awareness and cyber-physical systems

AbstractSafeguarding and conservation of cultural heritage is an important issue that requires both theoretical knowledge and practical implementation to effectively protect this valuable asset. In recent years, new technologies have enabled the development of advanced monitoring and control systems that can provide more precise and timely information about the condition of heritage buildings. Additionally, these systems can collect and analyze a large amount of data, allowing decision-makers to make informed decisions regarding maintenance and other management processes. With a situational awareness model proposed for heritage building conservation as a starting point, this work outlines how the design and implementation of a cyber-physical system to support conservation processes can be done. The model combines decisional processes that involve both humans and automated systems and can be used as a guideline for the realization of decision support systems for the management of heritage buildings. The paper provides a case study describing the steps for the realization of a cyber-physical system for automated monitoring and control at the Royal Palace of Carditello, a prominent cultural attraction in Italy.

Supporting Community First Responders in Aging in Place: An Action Design for a Community-Based Smart Activity Monitoring System

To maintain elderly individuals’ ability to live independently in their own homes, technologies such as sensors and the Internet of Things can be used to facilitate real-time monitoring and automate emergency detection and alerts to enable first responders to deliver help on time. This study examines the design problems associated with such activity monitoring systems, particularly how to support the situational awareness of community first responders who are located remotely and do not have professional healthcare knowledge. Based on a three-year action design research study that investigated a sensor-based monitoring system for elderly people deployed in a natural community setting, we develop abstract design knowledge. Four design principles aimed at enhancing responders’ perceptions, comprehension, and projection of information are proposed to promote informed decision-making and timely responses by drawing on the situational awareness model. In addition to the nascent design knowledge regarding remote activity monitoring in different instances, this study extends our knowledge of the community-based model by foregrounding its multiplicity and the active roles of community stakeholders in providing care to elderly people.

Situation identification in smart wearable computing systems based on machine learning and Context Space Theory

Wearable devices and smart sensors are increasingly adopted to monitor the behaviors of human and artificial agents. Many applications rely on the capability of such devices to recognize daily life activities performed by the monitored users in order to tailor their behaviors with respect to the occurring situations. Despite the constant evolution of smart sensing technologies and the numerous research in this field, an accurate recognition of in-the-wild situations still represents an open research challenge. This work proposes a novel approach for situation identification capable of recognizing the activities and the situations in which they occur in different environments and behavioral contexts, processing data acquired by wearable and environmental sensors.An architecture of a situation-aware wearable computing system is proposed, inspired by Endsley’s situation-awareness model, consisting of a two-step approach for situation identification. The approach first identifies the daily life activities via a learning-based technique. Simultaneously, the context in which the activities are performed is recognized using Context Space Theory. Finally, the fusion between the context state and the activities allows identifying the complex situations in which the user is acting. The knowledge regarding the situations forms the basis on which novel and smarter applications can be realized.The approach has been evaluated on the ExtraSensory public dataset and compared with state-of-the-art techniques, achieving an accuracy of 96% for the recognition of situations and with significantly low computational time, demonstrating the efficacy of the two-step situation identification approach.